code: def _get_q_value(
      self,
      model_out: TensorType,
      actions,
      net,
      state_in: List[TensorType],
      seq_lens: TensorType,
  ) -> (TensorType, List[TensorType]):
      # Continuous case -> concat actions to model_out.
      if (
          actions is not None
          and not model_out.get("obs_and_action_concatenated") is True
      ):
          # Make sure that, if we call this method twice with the same
          # input, we don't concatenate twice
          model_out["obs_and_action_concatenated"] = True
  
          if self.concat_obs_and_actions:
              model_out[SampleBatch.OBS] = torch.cat(
                  [model_out[SampleBatch.OBS], actions], dim=-1
              )
          else:
              model_out[SampleBatch.OBS] = force_list(
                  model_out[SampleBatch.OBS]
              ) + [actions]
  
      # Switch on training mode (when getting Q-values, we are usually in
      # training).
      model_out["is_training"] = True
  
      out, state_out = net(model_out, state_in, seq_lens)
      return out, state_out
  
file_path: ray/rllib/algorithms/sac/rnnsac_torch_model.py
imports: import gymnasium
  from typing import Optional, List, Dict
  from ray.rllib.algorithms.sac.sac_torch_model import SACTorchModel
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import override, force_list
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 3 for calling the RNNSACTorchModel obj's underlying member method code with example usage: obj._get_q_value(model_out, actions, net, state_in, seq_lens) and returns: out,  state_out